refactor: Lint agents-api (CI)

agents-api/agents_api/queries/docs/delete_doc.py

@@ -1,6 +1,7 @@
 """
 Timescale-based deletion of a doc record.
 """
+
 from typing import Literal
 from uuid import UUID
 

agents-api/agents_api/queries/docs/get_doc.py

@@ -1,6 +1,7 @@
 """
 Timescale-based retrieval of a single doc record.
 """
+
 from typing import Literal
 from uuid import UUID
 
@@ -41,7 +42,7 @@ async def get_doc(
     developer_id: UUID,
     doc_id: UUID,
     owner_type: Literal["user", "agent", "org"] | None = None,
-    owner_id: UUID | None = None
+    owner_id: UUID | None = None,
 ) -> tuple[str, list]:
     """
     Fetch a single doc, optionally constrained to a given owner.

agents-api/agents_api/queries/docs/list_docs.py

@@ -1,6 +1,7 @@
 """
 Timescale-based listing of docs with optional owner filter and pagination.
 """
+
 from typing import Literal
 from uuid import UUID
 

agents-api/agents_api/queries/docs/search_docs_by_embedding.py

@@ -2,10 +2,10 @@
 Timescale-based doc embedding search using the `embedding` column.
 """
 
-import asyncpg
-from typing import Literal, List
+from typing import List, Literal
 from uuid import UUID
 
+import asyncpg
 from beartype import beartype
 from fastapi import HTTPException
 from sqlglot import parse_one
@@ -32,6 +32,7 @@
 LIMIT $2;
 """).sql(pretty=True)
 
+
 @wrap_in_class(
     Doc,
     one=False,

agents-api/agents_api/queries/docs/search_docs_by_text.py

@@ -2,10 +2,10 @@
 Timescale-based doc text search using the `search_tsv` column.
 """
 
-import asyncpg
 from typing import Literal
 from uuid import UUID
 
+import asyncpg
 from beartype import beartype
 from fastapi import HTTPException
 from sqlglot import parse_one

agents-api/agents_api/queries/docs/search_docs_hybrid.py

@@ -3,36 +3,41 @@
 via a simple distribution-based score fusion or direct weighting in Python.
 """
 
-from typing import Literal, List
+from typing import List, Literal
 from uuid import UUID
 
 from beartype import beartype
 from fastapi import HTTPException
 
 from ...autogen.openapi_model import Doc
 from ..utils import run_concurrently
-from .search_docs_by_text import search_docs_by_text
 from .search_docs_by_embedding import search_docs_by_embedding
+from .search_docs_by_text import search_docs_by_text
+
 
 def dbsf_normalize(scores: List[float]) -> List[float]:
     """
     Example distribution-based normalization: clamp each score
     from (mean - 3*stddev) to (mean + 3*stddev) and scale to 0..1
     """
     import statistics
+
     if len(scores) < 2:
         return scores
     m = statistics.mean(scores)
     sd = statistics.pstdev(scores)  # population std
     if sd == 0:
         return scores
-    upper = m + 3*sd
-    lower = m - 3*sd
+    upper = m + 3 * sd
+    lower = m - 3 * sd
+
     def clamp_scale(v):
         c = min(upper, max(lower, v))
         return (c - lower) / (upper - lower)
+
     return [clamp_scale(s) for s in scores]
 
+
 @beartype
 def fuse_results(
     text_docs: List[Doc], embedding_docs: List[Doc], alpha: float
@@ -151,6 +156,7 @@ async def search_docs_hybrid(
     # text_results, embed_results = await run_concurrently([task1, task2])
     # Otherwise just do them in parallel with e.g. asyncio.gather:
     from asyncio import gather
+
     text_results, embed_results = await gather(*tasks)
 
     # fuse them